BetXplain: An Explanation-Annotated Dataset for Detecting Manipulative Betting Advertisements on Social Media

2026-06-25 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Advanced, quick

Summary

BetXplain is a newly introduced, explanation-annotated dataset designed to facilitate the automated detection of manipulative betting advertisements on social media. Collected from Instagram and Reddit, this dataset features manually annotated betting-related advertisements, identifying deceptive and manipulative practices. Crucially, it includes human-provided explanations detailing the reasoning behind each annotation, which supports research into explainable AI approaches for this domain. The accompanying work also analyzes common persuasive strategies in betting advertisements and explores their potential impact on users' mental well-being. This framework can enable practical applications such as browser plugins to warn users and automated web crawlers for regulatory monitoring of online promotions.

Key takeaway

For AI Scientists or Data Scientists developing content moderation tools, BetXplain offers a critical resource. You can utilize this explanation-annotated dataset to train and evaluate models for detecting manipulative betting advertisements, especially those impacting mental well-being. This enables you to build more transparent and explainable AI systems, potentially integrating them into browser plugins or regulatory monitoring crawlers to protect vulnerable users.

Key insights

BetXplain provides an explanation-annotated dataset to advance automated detection of manipulative social media betting ads and their mental health impacts.

Principles

• Annotated datasets drive automated detection.
• Human explanations enable explainable AI.
• Persuasive tactics affect mental health.

Method

Advertisements from Instagram and Reddit were manually annotated for manipulative practices, including classification labels and human-provided explanations.

In practice

• Develop browser plugins for user warnings.
• Build web crawlers for regulatory monitoring.

Topics

• BetXplain Dataset
• Manipulative Advertising
• Social Media Monitoring
• Explainable AI
• Mental Health Impact
• Content Moderation

Best for: Research Scientist, CTO, VP of Engineering/Data, AI Scientist, Data Scientist, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.